You're reading from Python Machine Learning - Third Edition

Product typeBook

Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Computing gradients via automatic differentiation and GradientTape

As you already know, optimizing NNs requires computing the gradients of the cost with respect to the NN weights. This is required for optimization algorithms such as stochastic gradient descent (SGD). In addition, gradients have other applications, such as diagnosing the network to find out why an NN model is making a particular prediction for a test example. Therefore, in this section, we will cover how to compute gradients of a computation with respect to some variables.

Computing the gradients of the loss with respect to trainable variables

TensorFlow supports automatic differentiation, which can be thought of as an implementation of the chain rule for computing gradients of nested functions. When we define a series of operations that results in some output or even intermediate tensors, TensorFlow provides a context for calculating gradients of these computed tensors with respect to its dependent nodes in the...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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You're reading from Python Machine Learning - Third Edition

Computing gradients via automatic differentiation and GradientTape

Computing the gradients of the loss with respect to trainable variables

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Authors (2)

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